Hệ thống phân loại mờ Takagi-Sugeno loại 2 khoảng dựa trên PSO và SVM cho việc nhận diện giới tính

Multimedia Tools and Applications - Tập 75 - Trang 987-1007 - 2014
Yijun Du1,2, Xiaobo Lu1,2, Lin Chen1,2, Weili Zeng1,2
1School of Automation, Southeast University, Nanjing, China
2Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing, China

Tóm tắt

Trong bài báo này, chúng tôi đề xuất một hệ thống phân loại mờ Takagi-Sugeno loại 2 khoảng (IT2T-SFCS) được học bởi tối ưu bầy đàn (PSO) và máy vector hỗ trợ (SVM) để tối ưu hóa các tham số điều kiện và hệ quả. Hệ thống IT2T-SFCS được xây dựng dựa trên các quy tắc mờ nếu-thì, trong đó các điều kiện là các tập mờ loại 2 khoảng và hệ quả là các phương trình trạng thái tuyến tính. Các điều kiện của IT2T-SFCS sử dụng kỹ thuật phân tích dữ liệu tự tổ chức mờ lặp (ISODATA) và PSO để học và tính toán các trung tâm tối ưu cũng như các độ rộng không chắc chắn của các hàm thành viên Gaussian. Các tham số hệ quả trong IT2T-SFCS được học thông qua SVM nhằm mục đích đạt được khả năng phân loại tổng quát cao hơn. Hệ thống IT2T-SFCS được đề xuất có khả năng xử lý trực tiếp các bất định, giảm thiểu những tác động của các bất định và đạt được hiệu suất tổng quát tốt hơn, kế thừa những lợi ích của hệ thống mờ loại 2 khoảng T-S và SVM. Để minh chứng, IT2T-SFCS được sử dụng như là một bộ phân loại trong nhận diện giới tính. Kết quả thực nghiệm cho thấy hiệu suất của IT2T-SFCS được đề xuất vượt trội hơn so với các bộ phân loại chính thống trước đó.

Từ khóa

#phân loại mờ #Takagi-Sugeno #tối ưu bầy đàn #máy vector hỗ trợ #nhận diện giới tính

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